code: def test_3conv_with_graph_break(self):
      class Model(nn.Module):
          def __init__(
              self,
              dim=512,
              patch_size=7,
              kernel_size=7,
              manual_graph_break=False,
          ):
              super().__init__()
              self.seq = nn.Sequential(
                  nn.Conv2d(
                      3,
                      dim,
                      kernel_size=patch_size,
                      stride=patch_size,
                      bias=False,
                  ),
                  nn.Conv2d(
                      dim,
                      dim,
                      kernel_size,
                      groups=dim,
                      padding="same",
                      bias=False,
                  ),
              )
              self.conv = nn.Conv2d(dim, dim, kernel_size=1, bias=False)
              self.manual_graph_break = manual_graph_break
  
          def forward(self, x):
              x = self.seq(x)
              if self.manual_graph_break:
                  torch._dynamo.graph_break()
              x = self.conv(x)
              return x
  
          def get_example_inputs(self):
              return (torch.randn(2, 3, 16, 16),)
  
      self.verify_accuracy_for_infer(Model)
  
imports: import copy
  import os
  import random
  import torch
  from torch import nn
  from torch._dynamo.test_case import run_tests, TestCase
  from torch._dynamo.utils import same
  from torch._inductor import config
  from torch.testing._internal.inductor_utils import HAS_CUDA
label_desc: Use image node_id 6 for calling the TestLayoutOptim obj's underlying member method code with example usage: obj.test_3conv_with_graph_break() and returns: x, torch